Learning temporal difference embeddings for biomedical hypothesis generation

Abstract

Motivation: Hypothesis generation (HG) refers to the discovery of meaningful implicit connections between disjoint scientific terms, which is of great significance for drug discovery, prediction of drug side effects and precision treatment. More recently, a few initial studies attempt to model the dynamic meaning of the terms or term pairs for HG. However, most existing methods still fail to accurately capture and utilize the dynamic evolution of scientific term relations.

Results: This article proposes a novel temporal difference embedding (TDE) learning framework to model the temporal difference information evolution of term-pair relations for predicting future interactions. Specifically, the HG problem is formulated as a future connectivity prediction task on a temporal sequence of a dynamic attributed graph. Our approach models both the local neighbor changes of the term-pairs and the changes of the global graph structure over time, learning local and global TDE of node-pairs, respectively. Future term-pair relations can be inferred in a recurrent network based on the local and global TDE. Experiments on three real-world biomedical term relationship datasets show the effectiveness and superiority of the proposed approach.

Availability and implementation: The data and source codes related to TDE are publicly available at https://github.com/Huiweizhou/TDE.

Supplementary information: Supplementary data are available at Bioinformatics online.